Optimized Well Planning Using 3D EM Inversion Results
Bibliographic record
Abstract
Abstract The use of one-dimensional (1D) electromagnetic (EM) inversion for well placement, reservoir mapping, and planning of multi-lateral wells is now quite common, but it is limited because it assumes continuity of resistivity in all directions except above and below the wellbore. Where formation and fluid distributions are not simple layer cake structures, 1D inversion does not reveal the lateral distribution of target zones. Structures with lateral variability require mapping of the geology in three dimensions (3D). Historically this has been done based on seismic data, but this has limited resolution. 3D EM inversion allows more refined well placement and reservoir mapping. Multi-frequency 3D inversion results in adjacent multi-lateral wellbores can be verified by superimposing the results to identify the same formation and fluid boundaries and can be used in re-planning trajectories of subsequent laterals to consider lateral changes in the position of resistivity boundaries. This paper presents results from a complex turbidite reservoir, developed using a multi-lateral well with three production branches. 1D EM inversion used in real-time displayed the vertical distribution of the sands, but did not indicate lateral variations, which are expected in this geological environment. Real-time ultra-deep azimuthal resistivity images provided a qualitative assessment of the lateral distribution of the target sands, indicating that the lateral position of the target would need to be considered when planning the second lateral. 3D inversion of memory data was performed within 48 hours of drilling the first lateral to understand the complex sand distribution. The distribution of the geobodies identified from the inversion results was used to re-plan the subsequent laterals for optimal placement and enhanced reservoir contact. All three laterals were inverted independently, providing overlapping datasets that showed similar structural features, giving confidence in the results. In a complex turbidite reservoir with discontinuous boundaries and significant lateral variations, true reservoir understanding requires 3D inversion of EM data. Multi-lateral wells provide an ideal opportunity to gain high confidence in the inversion workflow with repeatability of the results across multiple overlapping datasets. The reservoir understanding brought by this data enabled more sophisticated well planning and increased reservoir exposure in the subsequent laterals. When available in real-time, 3D EM inversion will facilitate azimuthal, as well as inclination, changes in a well path for optimal placement. The improved reservoir cognizance provided by 3D inversion of ultra-deep EM data allows lateral variation in the position of geobodies to be considered when planning multi-lateral wells. Previously, 1D inversion only allowed TVD changes to be considered. As many complex geological scenarios are 3D in nature, 3D EM inversion allows changes to the planned azimuth of a well path to also be considered for optimal well placement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".